KG-to-Text Generation with Slot-Attention and Link-Attention

Abstract

Slot attention has shown remarkable objectcentric representation learning performance in computer vision tasks without requiring any supervision. Despite its object-centric binding ability brought by compositional modelling, as a deterministic module, slot attention lacks the ability to generate novel scenes. In this paper, we propose the Slot-VAE, a generative model that integrates slot attention with the hierarchical VAE framework for object-centric structured scene generation. For each image, the model simultaneously infers a global scene representation to capture high-level scene structure and object-centric slot representations to embed individual object components. During generation, slot representations are generated from the global scene representation to ensure coherent scene structures. Our extensive evaluation of the scene generation ability indicates that Slot-VAE outperforms slot representation-based generative baselines in terms of sample quality and scene structure accuracy.

A Systematic Literature Review on Text Generation Using Deep Neural Network Models

“…Papers Human-centric (HC) [50], [53], [100] Machine-centric (MC) [60], [63]- [67], [83], [84], [86] [3], [69]- [71], [74], [95], [99], [122] [72], [73], [75]- [80] [21], [82], [90]- [92], [94], [102], [125] [6], [48], [49], [51], [52], [55], [56], [126] [18], [57], [58], [115], [119], [127], [128] [104], [112]- [114], [120], [129] [105], [106], [108], [110], [116], [117], [123] Both [5], [54], [59],…”

Sentence Graph Attention for Content-Aware Summarization

“…Currently, researchers are studying how to integrate neural networks with knowledge bases. Some research works focused on table-to-text [8][9][10]30], where a table that depicts a "plain" knowledge base is read by a sequence-to-sequence model and transformed into a text that reports (almost) all information occurring in the table. In Wang et al [30], the authors constructed both a latent graph over the entries of the table, looking to their position in the input, and an attention over them.…”

“…To solve this drawback, we examined the recent works on neural network models based on knowledge bases (KBs) [8][9][10][11][12][13][14]. In these research works, the knowledge graph created using an external KB was explored by the neural network to extract the relevant information (i.e., it selected one or more vertexes) via the attention mechanism, which focused on the relevant entities of the graph.…”

Using knowledge graphs and behaviour trees for feedback-aware presentation agents

“…The process of converting data represented as knowledge graphs into text is sometimes referred to as graph to text (Schmitt et al, 2020;, or KG to text (Schmitt et al, 2021;Wang et al, 2019). The term Data-to-Text typically refers to a more general group of tasks of which KG-to-text is part (Nan et al, 2021;Yin and Wan, 2022;Ji et al, 2023).…”